The hyperspectral remote sensing images (HSIs) are acquired to encompass the essential information of land objects through contiguous narrow spectral wavelength bands. The classification accuracy is not often satisfactory in a cost-effective way using the entire original HSI for practical applications. To enhance the classification result of HSIs the band reduction strategies are applied which can be divided into feature extraction and feature selection methods. PCA (Principal Component Analysis), a linear unsupervised statistical transformation, is frequently adopted for the extraction of features from HSIs. In this paper, PCA and SPCA (Segmented-PCA), SSPCA (Spectrally Segmented-PCA), FPCA (Folded-PCA) and MNF (Minimum Noise Fraction) as linear variants of PCA together with KPCA (Kernel-PCA) and KECA (kernel Entropy Component Analysis) as nonlinear variants of PCA have been investigated. The top transformed features were picked out using accumulation of variance for all other feature extraction methods except for MNF and KECA. MNF uses SNR (Signal-to-Noise Ratio) values and KECA employs Renyi quadratic entropy measurement for this purpose. The studied approaches are equated and analyzed for Indian Pine agricultural and urban Washington DC Mall HSI classification using SVM (Support Vector Machine) classifier. The experiment illustrates that the costly effective and improved classification performance of the feature extraction approaches over the performance using the entire original dataset. MNF offers the highest classification accuracy and FPCA offers the least space and time complexity with satisfactory classification result.

获取高光谱遥感图像 (HSI) 以通过连续的窄光谱波段来包含陆地物体的基本信息。在实际应用中使用整个原始 HSI 以经济高效的方式分类精度通常不能令人满意。为了提高 HSI 的分类结果，应用了频带缩减策略，该策略可以分为特征提取和特征选择方法。PCA（主成分分析）是一种线性无监督统计变换，经常用于从 HSI 中提取特征。在本文中，PCA和SPCA（Segmented-PCA），SSPCA（Spectrally Segmented-PCA），已经研究了作为 PCA 线性变体的 FPCA（折叠 PCA）和 MNF（最小噪声分数）以及作为 PCA 非线性变体的 KPCA（核-PCA）和 KECA（核熵分量分析）。除了 MNF 和 KECA 之外，所有其他特征提取方法都使用方差累积来挑选出顶部转换的特征。MNF 使用 SNR（信噪比）值，而 KECA 为此使用 Renyi 二次熵测量。使用 SVM（支持向量机）分类器对所研究的方法进行了等同和分析，用于印度松农业和城市华盛顿特区购物中心 HSI 分类。实验表明，与使用整个原始数据集的性能相比，特征提取方法的分类性能成本高且有效。